چکیده انگلیسی

This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine (FSVM). The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance. To decrease the negative influence of the auto-correlated and irrelevant variables, a key variable identification procedure using recursive feature elimination, based on the SVM is implemented, with time lags incorporated, before every classifier is trained, and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation. Performance comparisons are implemented among several kinds of multi-class decision machines, by which the effectiveness of the proposed approach is proved.

مقدمه انگلیسی

The impact of abnormal situations on the safety
and economic aspects of process operations is enormous.
Fault detection and diagnosis (FDD) has been
recognized as an important aspect of process operations:
the ultimate goal of FDD is to realize high level
autonomy in a dynamic system together with more
sophisticated control strategies.
In the past, extensive research efforts were accumulated
by developing model-based methods,
which were based on analytical redundancy (AR). The
faults were indicated with the residual signal got by
the difference between the measured output signal and
the output value of a nominal system model[ 1-31.
But model-based FDD depends heavily on the
system model, and it is very difficult to design these
kind of algorithms for nonlinear or uncertain systems[
4]. Knowledge-based methods have no need for
an analytical model, and rely on data-driven and
knowledge-based techniques to estimate the system
dynamics[5]. In many of these techniques, different
operating conditions including normal and abnormal
ones are treated as patterns. Then, a specific classifier
is applied to analyze the online measurement data and
to map them to a known class label for fault or normal
so that the current system condition is identified[4--6].
The major advantage of these direct FDD techniques
is their superior capability in identifying/classifying
the faults and versatility in white-boxhlack-box cases.
There have been many supervised classification
techniques such as, K-nearest neighbor (K")[7],
Fisher discriminant analysis (FDA)[5], artificial neural
networks (ANN)[8] and SVM[9] can be used in
this field. K" and FDA are linear classifiers, by
which only linear cases can be separated. ANN andSVM are apt to classify nonlineadlinear cases. But in
applications, it is very hard to decide the number of
hidden nodes in ANN, and another question is how to
avoid trapping into the local minimum point. New
theoretic directions are needed in solving these two
problems. SVM is a set of universal feed-forward
network-based classification algorithm combined with
kernel techniques essentially. SVM is based on the
statistical learning theory and structural risk minimization
(SRM) principle developed by Vapnik[ lo]. On
the basis of these two theories, there is only one minimum
in the SVM algorithm, and the structure of the
SVM network is fixed.
Application of SVMs to solve process engineering
problems is relatively new[5,11]. In Ref.[5], pairwise
SVM is used to classify a 3-class dataset in Tennessee
Eastman Process (TEP). On the basis of the
key variables identified by genetic algorithm combined
with Fisher discriminant analysis (GAFDA),
painvise SVM, achieved a relatively low misclassification
rate. Still many aspects may be improved in the
application. First, G m A is a very time-consuming
algorithm for identifying the key variables in 2-class
cases. To find the exact key variables, GA will be run
many times. The statistical results of GAS are outputs
to evaluate which variables are more informative. And
the time for searching for an optimal solution is a linear
function of a prefixed number of key variables[5].
If auto-con-elated data is considered, time lag should
be incorporated; and the number of prefixed key variables
increased will multiply with the time lag. So, a
more efficient key variable identification algorithm is
needed. In this article, accelerated recursive feature
elimination, based on a SVM is adopted as a substitute
for GA/FDA. Its performance has been proved cam-parable with GAEDA in Ref.[11], and here the number
of key variables is decided basically by 10-fold
cross-validation. Secondly, pairwise and one versus all
structural classifiers are discussed in Ref.[5]. But
when utilizing these two structural classifiers, there
are also unclassifiable regions. Here, a classifier
named FSVM based on pairwise SVM classifier and
fuzzy decision factor[l2-14] is used to make up this
blind point. Thirdly, the trial and error procedure is
used for tuning the SVM parameters in Ref.[5]; such
an approach, apart from consuming enormous time
may not really obtain the best possible performance.
In this study, sequential quadratic programming (SQP)
combined with radiudmargin bound of SVM
leave-one-out errors are used for tuning the SVM parameters.
In this article, a brief introduction to the methods
used in this study is given, including accelerated recursive
feature elimination by the SVM and FSVM,
the description of an estimate of generalization of errors
and how to tune SVM parameters by minimizing
this estimate. Finally, these three methods are combined
in a framework. The results obtained by considering
a 5-class problem from TEP[15,16] is discussed,
and conclusions are summarized.

نتیجه گیری انگلیسی

This study describes a classification methodology
based on SVMs, which offers superior classification
performance for fault diagnosis in chemical process
engineering. The method incorporates efficient parameter
tuning procedures based on minimization of
radiudmargin bound for SVM’s leave-one-out errors
into the multi-class classification strategy, using the
fuzzy decision factor. The datasets generated for the
TEP simulator were used to evaluate the classification
performance. To decrease the negative influence from
the auto-correlated and irrelevant variables, a key
variable identification procedure by recursive feature
elimination, based on an SVM is implemented, with
time lags incorporated before every classifier is
trained, and the number of relatively important variables
to every classifier is basically determined by
10-fold cross-validation. Performance comparisons
are implemented among four kinds of multi-class decision
machines, by which the effectiveness of the
proposed approach is proved.